Overview

This conference brings together data scientists, analysts, researchers, industry experts, and policymakers to discuss advances in data collection, processing, analysis, and visualization. It covers a wide range of topics from foundational preprocessing to advanced machine learning, explainable AI, and data-driven decision-making across sectors including healthcare, finance, marketing, and supply chains.

Structure and Sessions (Who Does What)

• Opening & Keynotes: Thought leaders provide overviews of emerging trends in AI-driven analytics, big data, and ethical considerations in data science.
• Technical Sessions: Peer-reviewed presentations on data preprocessing, statistical modeling, machine and deep learning, NLP, computer vision, anomaly detection, and real-time analytics.
• Workshops & Tutorials: Hands-on training in data wrangling, predictive modeling, explainable AI, cloud data platforms, and visualization techniques.
• Panel Discussions: Multi-disciplinary panels discuss challenges including data governance, privacy, AI automation, ethics, and industry applications.
• Poster Sessions: Researchers and students present innovative case studies, algorithms, and analytics frameworks.
• Awards & Closing: Recognitions for best research, practical innovations, and participant engagement.

Participate and Contribute (Who Benefits)

• Researchers & Academics: Share novel methodologies, receive feedback, and collaborate across disciplines.
• Industry Professionals & Data Practitioners: Discover latest tools, analytical approaches, and successful implementations.
• Students & Emerging Data Scientists: Present projects, learn skills, and network with mentors and peers.
• Policy Makers & Ethics Experts: Understand data privacy challenges and frameworks for responsible AI and analytics.

Benefits for Attendees

• Certificate of participation based on active engagement and feedback.
• Digital memento recognizing conference involvement.
• Publication opportunities for abstracts and full papers in indexed journals (Scopus, CrossRef, WoS, Elsevier, SCI).
• Awards for best paper, poster, and engagement contributions.
• Access to session recordings and materials post-event.
• Structured networking and mentorship opportunities.

Abstract Topics / CFP Tracks – Registration

• Data collection, cleaning, wrangling, and preprocessing
• Exploratory, descriptive, predictive, and prescriptive analytics
• Statistical modeling, machine learning, and deep learning
• Natural language processing and text analytics
• Computer vision and image analytics
• Big data platforms, streaming analytics, and IoT analytics
• Data mining, anomaly detection, and time series analysis
• Data visualization, storytelling, and business intelligence
• AI automation, explainable AI (XAI), and model validation
• Data governance, privacy, security, and ethics
• Recommender systems, customer analytics, and marketing analytics
• Healthcare, financial, supply chain, and HR analytics
• Social network and geospatial data analytics
• A/B testing and experimentation in analytics

Submissions undergo peer review; accepted abstracts and papers require registration for presentation and publication.

Detailed Program Grid (2 Days) — Online Conference (with Breaks)

This comprehensive program cultivates impactful research dissemination, skill development, and collaborative innovation in data analytics and data science fields.